Deep Learning-Based Traffic Safety Solution for a Mixture of Autonomous and Manual Vehicles in a 5G-Enabled Intelligent Transportation System

2020 ◽  
pp. 1-11
Author(s):  
Keping Yu ◽  
Long Lin ◽  
Mamoun Alazab ◽  
Liang Tan ◽  
Bo Gu
Keyword(s):  
Deep Learning ◽  
Traffic Safety ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System

scholarly journals Deep Intelligent Prediction Network: A Novel Deep Learning Based Prediction Model on Spatiotemporal Characteristics and Location Based Services for Big Data Driven Intelligent Transportation System

2019 ◽  
Vol 8 (11) ◽  
pp. 845-849
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Recommendation System ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Transportation Systems ◽  
Location Based Services ◽  
Traffic Data ◽  
Spatiotemporal Characteristics

The concept of big Data for intelligent transportation system has been employed for traffic management on dealing with dynamic traffic environments. Big data analytics helps to cope with large amount of storage and computing resources required to use mass traffic data effectively. However these traditional solutions brings us unprecedented opportunities to manage transportation data but it is inefficient for building the next-generation intelligent transportation systems as Traffic data exploring in velocity and volume on various characteristics. In this article, a new deep intelligent prediction network has been introduced that is hierarchical and operates with spatiotemporal characteristics and location based service on utilizing the Sensor and GPS data of the vehicle in the real time. The proposed model employs deep learning architecture to predict potential road clusters for passengers. It is injected as recommendation system to passenger in terms of mobile apps and hardware equipment employment on the vehicle incorporating location based services models to seek available parking slots, traffic free roads and shortest path for reach destination and other services in the specified path etc. The underlying the traffic data is classified into clusters with extracting set of features on it. The deep behavioural network processes the traffic data in terms of spatiotemporal characteristics to generate the traffic forecasting information, vehicle detection, autonomous driving and driving behaviours. In addition, markov model is embedded to discover the hidden features .The experimental results demonstrates that proposed approaches achieves better results against state of art approaches on the performance measures named as precision, execution time, feasibility and efficiency.

scholarly journals Implementasi Fasilitas Intelligent Transportation System (ITS) Di Kota Palangka Raya

2020 ◽  
Vol 8 (2) ◽  
pp. 72-78
Author(s):  
Devia Devia ◽  
Prihanika Prihanika
Keyword(s):  
Control System ◽  
Traffic Safety ◽  
Traffic Control ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Travel Speed ◽  
Transportation System ◽  
Transportation Systems ◽  
Traffic Control System ◽  
The City

The movement of people and goods is increasing in line with economic growth in society. This causes the potential for increased transportation activities in the City of Palangka Raya so it needs efforts to improve adequate transportation facilities and infrastructure. The application of technology-based Intelligent Transportation System (ITS) in Palangka Raya City is needed so that the management of the transportation system becomes more effective and efficient. This paper provides an overview of the application of ITS facilities and types in Palangka Raya City and provides recommendations for the use of new ITS facilities or optimizing existing technology so that ITS facilities can be utilized by stakeholders in traffic management and transportation systems in Palangka Raya City. Based on observations of the application of ITS in the City of Palangka Raya is applied to improve the performance of intersections and road services. The type of ITS facility is the Area Traffic Control System (ATCS), which is a vehicle traffic control system at the signal intersection to increase travel speed and travel time so that delays in travel can be minimized. It is also expected that the implementation of ITS in Palangkaraya City can also optimize the performance of public transport and traffic safety as well as the collaboration between stakeholders so that the improvement of the integrated transportation system can be well integrated.

Modeling Traffic Safety Benefits of Intelligent Transportation System Technologies at Truck Inspection Facilities: Microscopic Simulation

2001 ◽  
Vol 1779 (1) ◽  
pp. 173-181 ◽  
Author(s):  
Anthony A. Saka ◽  
Richard A. Glassco
Keyword(s):  
High Risk ◽  
Simulation Model ◽  
Traffic Safety ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Primary Objective ◽  
Percent Reduction ◽  
Microscopic Simulation ◽  
Traffic Pattern

A microscopic simulation model was developed to capture the traffic safety benefits of using intelligent transportation system (ITS) technologies, including weigh-in-motion scales with variable message signs, at truck inspection facilities. The development of the simulation model was motivated by prevalent safety concerns at congested truck inspection facilities nationwide. Three primary safety components (roadway, driver, and vehicle) were considered in the model. The roadway component focuses on the varying size of truck queues at inspection facilities and safety implications. The driver component captures key human factor elements and their variability, including distributions for perception-reaction time, speed, gap acceptance, headway, and braking characteristics. The vehicular component incorporates the size distribution of vehicles (trucks and nontrucks), proportion of trucks with defective braking systems, and their safety implications with respect to stopping distance. The primary objective for the model is to depict variations in traffic pattern for baseline (pre-ITS) and post-ITS situations. Measures of effectiveness used for evaluating traffic benefits of using ITS technologies include percent reduction in sudden deceleration of vehicles resulting from shock wave phenomena and percent reduction in duration of truck-queue overflow resulting from a high traffic intensity. Results from simulation runs support the hypothesis that the use of ITS technologies at truck inspection facilities significantly reduces the frequency of experiencing the high-risk traffic phenomena (e.g., hard braking and truck-queue overflow). The postulation is made that the reduction in the frequency of high-risk phenomena will translate into a decrease in the likelihood of experiencing crash-related incidents in the vicinity of truck inspection facilities.

scholarly journals Design and Implementation of Intelligent Vehicle Control System Based on Internet of Things and Intelligent Transportation

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Zhihui Hu ◽  
Hai Tang
Keyword(s):  
Internet Of Things ◽  
Traffic Safety ◽  
Intelligent Transportation System ◽  
New Technologies ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Economic Losses ◽  
The Road ◽  
Use Of Resources ◽  
Internet Of Things Technology

With the improvement of urbanization and the continuous expansion of transportation scale, traffic problem has become an important problem in our life. How to ensure traffic safety has become the key issue for the government to implement social management. Nowadays, Internet of Things (IOT) technology is widely used in the industrial technology field. It will have a great impact on human production and life. Intelligent transportation system is a research field involving many high and new technologies. This paper proposes an intelligent transportation system based on Internet of Things technology. This paper presents the optimal design structure of intelligent transportation system based on Internet of Things technology. The experimental results show that the intelligent transportation system can effectively realize the information interaction between the vehicle and the control center and understand the road conditions in advance. At the same time, the intelligent transportation system can improve the driving speed of vehicles on the road, make effective use of resources, reduce economic losses during vehicle operation, and reduce air pollution caused by gasoline emission.

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